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# Text

### THE POWER OF GOALS.

The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. To maintain a goal expectation of 1.2 goals for each side, I gave each shot a 10% likelihood of success. So it is an artificial situation, but hopefully a test of the effect of goal expectation being unevenly spread among varying shot quantity. Potentially, team A, based on just two goal attempts can only score a maximum of two goals in a shot THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is a### handy

THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 to### H1001.

THE POWER OF GOALS.: LLORIS, THE BEST WITH ROOM TO IMPROVE? An average keeper gets more than a look in in this subset and the average model equals or beats Lloris' far post, on target actual outcome around 22% of the time. That's still ok, but perhaps suggests that even the very best have room to improve. Below I've stitched together a handful of Lloris' attempts to keep out far post, cross### shots to

THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or her### actual output.

### THE POWER OF GOALS.

The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. To maintain a goal expectation of 1.2 goals for each side, I gave each shot a 10% likelihood of success. So it is an artificial situation, but hopefully a test of the effect of goal expectation being unevenly spread among varying shot quantity. Potentially, team A, based on just two goal attempts can only score a maximum of two goals in a shot THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is a### handy

THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 to### H1001.

THE POWER OF GOALS.: LLORIS, THE BEST WITH ROOM TO IMPROVE? An average keeper gets more than a look in in this subset and the average model equals or beats Lloris' far post, on target actual outcome around 22% of the time. That's still ok, but perhaps suggests that even the very best have room to improve. Below I've stitched together a handful of Lloris' attempts to keep out far post, cross### shots to

THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or her### actual output.

THE POWER OF GOALS.: STATE OF PLAY 2020 Ian’s recent appearances in both the spoken and written media has not only highlighted the importance of an integrated approach to squad building that utilizes a data driven approach, alongside more traditional methods, it has also given a small glimpse into the analytical methods employed. THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Based on the data and the individual pass expectations, Bolton had about a 1% chance of completing such a move and the last four passes were among the sequence's most difficult attempts. Eagles had around a 10% chance of scoring with his effort. Once we put all these numbers together it quickly becomes apparent why football is a low scoring### sport!

THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: QUANTIFYING THE VALUE OF EVERY PASS This represents the starting point of every successful pass. The plot is best used in conjunction with video analysis, but you can quickly see that Rice's sphere of influence is concentrated broadly in front of the back four and across the line, but he also delivers an impressive range of threatening passing options mid way inside the opposition half and just leftfield. THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: INDIVIDUAL GOAL VALUES FOR THE EPL Firstly a brief description of the methodology. The win probability for each team is tracked on a minute by minute basis throughout each EPL game.At any point in a game a team will have a probability of winning the game outright and an associated probability of drawing the game.By multiplying the probability of winning the game by 3 and the probability of drawing by 1 and adding the results THE POWER OF GOALS.: HOW TO FRAME AN INDIVIDUAL MATCH OUTCOME. Here's some representative figures. Home teams are scoring 0.25 goals per game more than visitors, 1.49 compared to 1.24. The average game has 1.37 expected goals per team. THE POWER OF GOALS.: SCORING EFFICIENCY AND CURRENT SCORE. The tie appeared remarkable for many reasons.Not only had Chelsea played over half the second leg with just ten men and Messi had missed from the spot,but they had also enjoyed less than twenty percent of the possession and had been out shot by a ratio of 3:1over both legs.They had managed just 3 shots compared to Barca's 14 in their 1-0 win at Stamford Bridge and had fared only THE POWER OF GOALS.: OCTOBER 2016 At the dawn of footballing time, managers were lasting on average for around 150 matches, now it's down to about 50. Success rate obviously plays a part in perceived managerial talent and Zenga's so so 47% success rate would typically entitle him to at least a season of honest toil, rather than the 17 matches he was actually granted. THE POWER OF GOALS.: THE 2016 NFL REGULAR SEASON DONE These traits are often described as "knowing how to win", but they are almost always just random..or possibly cheating. Based on how teams with these flags against their record did in the subsequent season over the last decade, the Carolina Panthers (15-1)### THE POWER OF GOALS.

The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. To maintain a goal expectation of 1.2 goals for each side, I gave each shot a 10% likelihood of success. So it is an artificial situation, but hopefully a test of the effect of goal expectation being unevenly spread among varying shot quantity. Potentially, team A, based on just two goal attempts can only score a maximum of two goals in a shot THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is a### handy

THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 to### H1001.

THE POWER OF GOALS.: LLORIS, THE BEST WITH ROOM TO IMPROVE? An average keeper gets more than a look in in this subset and the average model equals or beats Lloris' far post, on target actual outcome around 22% of the time. That's still ok, but perhaps suggests that even the very best have room to improve. Below I've stitched together a handful of Lloris' attempts to keep out far post, cross### shots to

THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or her### actual output.

### THE POWER OF GOALS.

The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. To maintain a goal expectation of 1.2 goals for each side, I gave each shot a 10% likelihood of success. So it is an artificial situation, but hopefully a test of the effect of goal expectation being unevenly spread among varying shot quantity. Potentially, team A, based on just two goal attempts can only score a maximum of two goals in a shot THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is a### handy

THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 to### H1001.

THE POWER OF GOALS.: LLORIS, THE BEST WITH ROOM TO IMPROVE? An average keeper gets more than a look in in this subset and the average model equals or beats Lloris' far post, on target actual outcome around 22% of the time. That's still ok, but perhaps suggests that even the very best have room to improve. Below I've stitched together a handful of Lloris' attempts to keep out far post, cross### shots to

THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or her### actual output.

THE POWER OF GOALS.: STATE OF PLAY 2020 Ian’s recent appearances in both the spoken and written media has not only highlighted the importance of an integrated approach to squad building that utilizes a data driven approach, alongside more traditional methods, it has also given a small glimpse into the analytical methods employed. THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Based on the data and the individual pass expectations, Bolton had about a 1% chance of completing such a move and the last four passes were among the sequence's most difficult attempts. Eagles had around a 10% chance of scoring with his effort. Once we put all these numbers together it quickly becomes apparent why football is a low scoring### sport!

THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: QUANTIFYING THE VALUE OF EVERY PASS This represents the starting point of every successful pass. The plot is best used in conjunction with video analysis, but you can quickly see that Rice's sphere of influence is concentrated broadly in front of the back four and across the line, but he also delivers an impressive range of threatening passing options mid way inside the opposition half and just leftfield. THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: INDIVIDUAL GOAL VALUES FOR THE EPL Firstly a brief description of the methodology. The win probability for each team is tracked on a minute by minute basis throughout each EPL game.At any point in a game a team will have a probability of winning the game outright and an associated probability of drawing the game.By multiplying the probability of winning the game by 3 and the probability of drawing by 1 and adding the results THE POWER OF GOALS.: HOW TO FRAME AN INDIVIDUAL MATCH OUTCOME. Here's some representative figures. Home teams are scoring 0.25 goals per game more than visitors, 1.49 compared to 1.24. The average game has 1.37 expected goals per team. THE POWER OF GOALS.: SCORING EFFICIENCY AND CURRENT SCORE. The tie appeared remarkable for many reasons.Not only had Chelsea played over half the second leg with just ten men and Messi had missed from the spot,but they had also enjoyed less than twenty percent of the possession and had been out shot by a ratio of 3:1over both legs.They had managed just 3 shots compared to Barca's 14 in their 1-0 win at Stamford Bridge and had fared only THE POWER OF GOALS.: OCTOBER 2016 At the dawn of footballing time, managers were lasting on average for around 150 matches, now it's down to about 50. Success rate obviously plays a part in perceived managerial talent and Zenga's so so 47% success rate would typically entitle him to at least a season of honest toil, rather than the 17 matches he was actually granted. THE POWER OF GOALS.: THE 2016 NFL REGULAR SEASON DONE These traits are often described as "knowing how to win", but they are almost always just random..or possibly cheating. Based on how teams with these flags against their record did in the subsequent season over the last decade, the Carolina Panthers (15-1)### THE POWER OF GOALS.

The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: PREDICTING AND EXPLAINING.HOW TO USE The first choice to make is to decide how to measure success in soccer.Wins are the obvious and overwhelmingly popular selection,but this way fails to account for around 28% of the games that result in a draw.Therefore I have used THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. To maintain a goal expectation of 1.2 goals for each side, I gave each shot a 10% likelihood of success. So it is an artificial situation, but hopefully a test of the effect of goal expectation being unevenly spread among varying shot quantity. Potentially, team A, based on just two goal attempts can only score a maximum of two goals in a shot THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is a### handy

THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 to### H1001.

THE POWER OF GOALS.: HOW RED CARDS AFFECT A SOCCER MATCH. One of the most effective ways to model the expected progress of a soccer match is to calculate the average number of goals each team can expect to score at today's venue and against today's opponents.We can then use the Poisson distribution (with few tweaks) to calculate a multitude of predictions about the likely route the game will take.This is the basis for the game graphs on this site THE POWER OF GOALS.: QUICK AND EASY GAME STATES FOR FOOTBALL. One of the more glaring omissions in attempting to make sense of the huge increase in available football data relates to a lack of context. A priority during one stage of a match may become less so as the game progresses and often the driving force for change will be the game### state.

THE POWER OF GOALS.: MANCHESTER CITY AND UNITED.A TALE OF As the likely destination for the Premiership title heads towards it's climax and a possibly decisive derby meeting at the City of Manchester Stadium in late April,it's unsurprising that almost every contentious incident involved both Manchester teams are coming under scrutiny.### THE POWER OF GOALS.

The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: PREDICTING AND EXPLAINING.HOW TO USE The first choice to make is to decide how to measure success in soccer.Wins are the obvious and overwhelmingly popular selection,but this way fails to account for around 28% of the games that result in a draw.Therefore I have used THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. To maintain a goal expectation of 1.2 goals for each side, I gave each shot a 10% likelihood of success. So it is an artificial situation, but hopefully a test of the effect of goal expectation being unevenly spread among varying shot quantity. Potentially, team A, based on just two goal attempts can only score a maximum of two goals in a shot THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is a### handy

THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 to### H1001.

THE POWER OF GOALS.: HOW RED CARDS AFFECT A SOCCER MATCH. One of the most effective ways to model the expected progress of a soccer match is to calculate the average number of goals each team can expect to score at today's venue and against today's opponents.We can then use the Poisson distribution (with few tweaks) to calculate a multitude of predictions about the likely route the game will take.This is the basis for the game graphs on this site THE POWER OF GOALS.: QUICK AND EASY GAME STATES FOR FOOTBALL. One of the more glaring omissions in attempting to make sense of the huge increase in available football data relates to a lack of context. A priority during one stage of a match may become less so as the game progresses and often the driving force for change will be the game### state.

THE POWER OF GOALS.: MANCHESTER CITY AND UNITED.A TALE OF As the likely destination for the Premiership title heads towards it's climax and a possibly decisive derby meeting at the City of Manchester Stadium in late April,it's unsurprising that almost every contentious incident involved both Manchester teams are coming under scrutiny. THE POWER OF GOALS.: STATE OF PLAY 2020 Ian’s recent appearances in both the spoken and written media has not only highlighted the importance of an integrated approach to squad building that utilizes a data driven approach, alongside more traditional methods, it has also given a small glimpse into the analytical methods employed. THE POWER OF GOALS.: QUANTIFYING THE VALUE OF EVERY PASS This represents the starting point of every successful pass. The plot is best used in conjunction with video analysis, but you can quickly see that Rice's sphere of influence is concentrated broadly in front of the back four and across the line, but he also delivers an impressive range of threatening passing options mid way inside the opposition half and just leftfield. THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Based on the data and the individual pass expectations, Bolton had about a 1% chance of completing such a move and the last four passes were among the sequence's most difficult attempts. Eagles had around a 10% chance of scoring with his effort. Once we put all these numbers together it quickly becomes apparent why football is a low scoring### sport!

THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or her### actual output.

THE POWER OF GOALS.: QUICK AND EASY GAME STATES FOR FOOTBALL. One of the more glaring omissions in attempting to make sense of the huge increase in available football data relates to a lack of context. A priority during one stage of a match may become less so as the game progresses and often the driving force for change will be the game### state.

THE POWER OF GOALS.: INDIVIDUAL GOAL VALUES FOR THE EPL Firstly a brief description of the methodology. The win probability for each team is tracked on a minute by minute basis throughout each EPL game.At any point in a game a team will have a probability of winning the game outright and an associated probability of drawing the game.By multiplying the probability of winning the game by 3 and the probability of drawing by 1 and adding the results THE POWER OF GOALS.: EVALUATING EPL DEFENDERS. The attributes of attacking players in whichever sport are usually easier to quantify than those of defenders.Individual stats such as shots,shots on target are readily available and are strongly correlated to goals scored,which in turn correlate with team success. THE POWER OF GOALS.: LUCK CAN MAKE AN AVERAGE TEAM APPEAR It is very easy to be drawn to extreme performances, either good or bad when looking at the analytical side of football. Exceptional performance stands out, is always highlighted in the media and talked### about by the fans.

THE POWER OF GOALS.: A DECADE OF STEVEN GERRARD AS A Much of the current internet football discussion revolves around the transfer window and the destination of big name players to pastures new. Understandably, speculation about the likely impact of a newly acquired player on the fortunes of his new employers is rife.### THE POWER OF GOALS.

The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: PREDICTING AND EXPLAINING.HOW TO USE The first choice to make is to decide how to measure success in soccer.Wins are the obvious and overwhelmingly popular selection,but this way fails to account for around 28% of the games that result in a draw.Therefore I have used THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is a### handy

THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. To maintain a goal expectation of 1.2 goals for each side, I gave each shot a 10% likelihood of success. So it is an artificial situation, but hopefully a test of the effect of goal expectation being unevenly spread among varying shot quantity. Potentially, team A, based on just two goal attempts can only score a maximum of two goals in a shot THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 to### H1001.

THE POWER OF GOALS.: HOW RED CARDS AFFECT A SOCCER MATCH. One of the most effective ways to model the expected progress of a soccer match is to calculate the average number of goals each team can expect to score at today's venue and against today's opponents.We can then use the Poisson distribution (with few tweaks) to calculate a multitude of predictions about the likely route the game will take.This is the basis for the game graphs on this site THE POWER OF GOALS.: MANCHESTER CITY AND UNITED.A TALE OF As the likely destination for the Premiership title heads towards it's climax and a possibly decisive derby meeting at the City of Manchester Stadium in late April,it's unsurprising that almost every contentious incident involved both Manchester teams are coming under scrutiny. THE POWER OF GOALS.: DO EARLY RISERS SEE LESS CLEAN SHEETS? Non league Luton Town equalled a near 100 year old record by playing in the FA Cup sixth round on Saturday. Their feat was overshadowed on### two fronts.

### THE POWER OF GOALS.

The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: PREDICTING AND EXPLAINING.HOW TO USE The first choice to make is to decide how to measure success in soccer.Wins are the obvious and overwhelmingly popular selection,but this way fails to account for around 28% of the games that result in a draw.Therefore I have used THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is a### handy

THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. To maintain a goal expectation of 1.2 goals for each side, I gave each shot a 10% likelihood of success. So it is an artificial situation, but hopefully a test of the effect of goal expectation being unevenly spread among varying shot quantity. Potentially, team A, based on just two goal attempts can only score a maximum of two goals in a shot THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 to### H1001.

THE POWER OF GOALS.: HOW RED CARDS AFFECT A SOCCER MATCH. One of the most effective ways to model the expected progress of a soccer match is to calculate the average number of goals each team can expect to score at today's venue and against today's opponents.We can then use the Poisson distribution (with few tweaks) to calculate a multitude of predictions about the likely route the game will take.This is the basis for the game graphs on this site THE POWER OF GOALS.: MANCHESTER CITY AND UNITED.A TALE OF As the likely destination for the Premiership title heads towards it's climax and a possibly decisive derby meeting at the City of Manchester Stadium in late April,it's unsurprising that almost every contentious incident involved both Manchester teams are coming under scrutiny. THE POWER OF GOALS.: DO EARLY RISERS SEE LESS CLEAN SHEETS? Non league Luton Town equalled a near 100 year old record by playing in the FA Cup sixth round on Saturday. Their feat was overshadowed on### two fronts.

THE POWER OF GOALS.: STATE OF PLAY 2020 Ian’s recent appearances in both the spoken and written media has not only highlighted the importance of an integrated approach to squad building that utilizes a data driven approach, alongside more traditional methods, it has also given a small glimpse into the analytical methods employed. THE POWER OF GOALS.: QUANTIFYING THE VALUE OF EVERY PASS This represents the starting point of every successful pass. The plot is best used in conjunction with video analysis, but you can quickly see that Rice's sphere of influence is concentrated broadly in front of the back four and across the line, but he also delivers an impressive range of threatening passing options mid way inside the opposition half and just leftfield. THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Based on the data and the individual pass expectations, Bolton had about a 1% chance of completing such a move and the last four passes were among the sequence's most difficult attempts. Eagles had around a 10% chance of scoring with his effort. Once we put all these numbers together it quickly becomes apparent why football is a low scoring### sport!

THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or her### actual output.

THE POWER OF GOALS.: QUICK AND EASY GAME STATES FOR FOOTBALL. One of the more glaring omissions in attempting to make sense of the huge increase in available football data relates to a lack of context. A priority during one stage of a match may become less so as the game progresses and often the driving force for change will be the game### state.

THE POWER OF GOALS.: INDIVIDUAL GOAL VALUES FOR THE EPL Firstly a brief description of the methodology. The win probability for each team is tracked on a minute by minute basis throughout each EPL game.At any point in a game a team will have a probability of winning the game outright and an associated probability of drawing the game.By multiplying the probability of winning the game by 3 and the probability of drawing by 1 and adding the results THE POWER OF GOALS.: EVALUATING EPL DEFENDERS. The attributes of attacking players in whichever sport are usually easier to quantify than those of defenders.Individual stats such as shots,shots on target are readily available and are strongly correlated to goals scored,which in turn correlate with team success. THE POWER OF GOALS.: LUCK CAN MAKE AN AVERAGE TEAM APPEAR It is very easy to be drawn to extreme performances, either good or bad when looking at the analytical side of football. Exceptional performance stands out, is always highlighted in the media and talked### about by the fans.

THE POWER OF GOALS.: A DECADE OF STEVEN GERRARD AS A Much of the current internet football discussion revolves around the transfer window and the destination of big name players to pastures new. Understandably, speculation about the likely impact of a newly acquired player on the fortunes of his new employers is rife.### THE POWER OF GOALS.

Stoke Highlight the Art of Crossing. Two Stoke City games, two headers, two goals and a duo of 1-0 wins not only demonstrates the fine lines that can separate six points from two in a low scoring sport, such as football, but also the important role still played by crosses in the modern game. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS There's a lot of numbers, so it's colour coded, blue is good, red is not so, although the jury is still out on the final column. First numerical column is the cumulative increase in NSxG by each player's### successful passes.

THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 to### H1001.

THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. The simulation may be too abstract in that when a goal is scored will change a team's style and the number of opportunities they create. A side capable of having ten shots in a match can more easily have eleven than a side with two chances can have a third. THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. By contrast with defenders,the correlation between team success and successful passes made by a team's forwards is much weaker.Surprisingly,it would seem that if an average team could chose to improve the passing of either it's defenders or it's forwards by the same amount relative to the league,it would be better off in win terms to plump for the defenders.Intuitively,this seems wrong.As a THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or her### actual output.

THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Much of the previous detail could probably have been guessed at without looking at the actual data. Manchester City are an expensively assembled side who have quality players throughout with the ability to play a possession based style, while Bolton are traditionally a much more direct team who have recognized the profit to be reaped from delivering the ball rapidly into dangerous areas of the THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. In the previous post, I described a simple method to use expected or real goals to estimate the average number of goals each team might score and allow in a single game at a certain venue and hence derive the win/draw loss percentages for the game via a Poisson.### THE POWER OF GOALS.

Stoke Highlight the Art of Crossing. Two Stoke City games, two headers, two goals and a duo of 1-0 wins not only demonstrates the fine lines that can separate six points from two in a low scoring sport, such as football, but also the important role still played by crosses in the modern game. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS There's a lot of numbers, so it's colour coded, blue is good, red is not so, although the jury is still out on the final column. First numerical column is the cumulative increase in NSxG by each player's### successful passes.

THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 to### H1001.

THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. The simulation may be too abstract in that when a goal is scored will change a team's style and the number of opportunities they create. A side capable of having ten shots in a match can more easily have eleven than a side with two chances can have a third. THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. By contrast with defenders,the correlation between team success and successful passes made by a team's forwards is much weaker.Surprisingly,it would seem that if an average team could chose to improve the passing of either it's defenders or it's forwards by the same amount relative to the league,it would be better off in win terms to plump for the defenders.Intuitively,this seems wrong.As a THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or her### actual output.

THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Much of the previous detail could probably have been guessed at without looking at the actual data. Manchester City are an expensively assembled side who have quality players throughout with the ability to play a possession based style, while Bolton are traditionally a much more direct team who have recognized the profit to be reaped from delivering the ball rapidly into dangerous areas of the THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. In the previous post, I described a simple method to use expected or real goals to estimate the average number of goals each team might score and allow in a single game at a certain venue and hence derive the win/draw loss percentages for the game via a Poisson.### THE POWER OF GOALS.

Stoke Highlight the Art of Crossing. Two Stoke City games, two headers, two goals and a duo of 1-0 wins not only demonstrates the fine lines that can separate six points from two in a low scoring sport, such as football, but also the important role still played by crosses in the modern game. THE POWER OF GOALS.: STATE OF PLAY 2020 Ian’s recent appearances in both the spoken and written media has not only highlighted the importance of an integrated approach to squad building that utilizes a data driven approach, alongside more traditional methods, it has also given a small glimpse into the analytical methods employed. THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: QUANTIFYING THE VALUE OF EVERY PASS This represents the starting point of every successful pass. The plot is best used in conjunction with video analysis, but you can quickly see that Rice's sphere of influence is concentrated broadly in front of the back four and across the line, but he also delivers an impressive range of threatening passing options mid way inside the opposition half and just leftfield. THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. In the previous post, I described a simple method to use expected or real goals to estimate the average number of goals each team might score and allow in a single game at a certain venue and hence derive the win/draw loss percentages for the game via a Poisson. THE POWER OF GOALS.: HOW TO FRAME AN INDIVIDUAL MATCH OUTCOME. Here's some representative figures. Home teams are scoring 0.25 goals per game more than visitors, 1.49 compared to 1.24. The average game has 1.37 expected goals per team. THE POWER OF GOALS.: INDIVIDUAL GOAL VALUES FOR THE EPL Firstly a brief description of the methodology. The win probability for each team is tracked on a minute by minute basis throughout each EPL game.At any point in a game a team will have a probability of winning the game outright and an associated probability of drawing the game.By multiplying the probability of winning the game by 3 and the probability of drawing by 1 and adding the results THE POWER OF GOALS.: MEASURING THE MEASURERS. Only a few days to go before the eagerly awaited Olympic football kicks off in London and elsewhere, featuring hybrid versions of top### international sides

THE POWER OF GOALS.: SCORING EFFICIENCY AND CURRENT SCORE. The tie appeared remarkable for many reasons.Not only had Chelsea played over half the second leg with just ten men and Messi had missed from the spot,but they had also enjoyed less than twenty percent of the possession and had been out shot by a ratio of 3:1over both legs.They had managed just 3 shots compared to Barca's 14 in their 1-0 win at Stamford Bridge and had fared only THE POWER OF GOALS.: THE 2016 NFL REGULAR SEASON DONE These traits are often described as "knowing how to win", but they are almost always just random..or possibly cheating. Based on how teams with these flags against their record did in the subsequent season over the last decade, the Carolina Panthers (15-1)### THE POWER OF GOALS.

The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. To maintain a goal expectation of 1.2 goals for each side, I gave each shot a 10% likelihood of success. So it is an artificial situation, but hopefully a test of the effect of goal expectation being unevenly spread among varying shot quantity. Potentially, team A, based on just two goal attempts can only score a maximum of two goals in a shot THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is a### handy

THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 to### H1001.

THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Based on the data and the individual pass expectations, Bolton had about a 1% chance of completing such a move and the last four passes were among the sequence's most difficult attempts. Eagles had around a 10% chance of scoring with his effort. Once we put all these numbers together it quickly becomes apparent why football is a low scoring### sport!

THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or her### actual output.

### THE POWER OF GOALS.

The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. To maintain a goal expectation of 1.2 goals for each side, I gave each shot a 10% likelihood of success. So it is an artificial situation, but hopefully a test of the effect of goal expectation being unevenly spread among varying shot quantity. Potentially, team A, based on just two goal attempts can only score a maximum of two goals in a shot THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is a### handy

THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 to### H1001.

THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Based on the data and the individual pass expectations, Bolton had about a 1% chance of completing such a move and the last four passes were among the sequence's most difficult attempts. Eagles had around a 10% chance of scoring with his effort. Once we put all these numbers together it quickly becomes apparent why football is a low scoring### sport!

THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or her### actual output.

THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Based on the data and the individual pass expectations, Bolton had about a 1% chance of completing such a move and the last four passes were among the sequence's most difficult attempts. Eagles had around a 10% chance of scoring with his effort. Once we put all these numbers together it quickly becomes apparent why football is a low scoring### sport!

THE POWER OF GOALS.: QUANTIFYING THE VALUE OF EVERY PASS This represents the starting point of every successful pass. The plot is best used in conjunction with video analysis, but you can quickly see that Rice's sphere of influence is concentrated broadly in front of the back four and across the line, but he also delivers an impressive range of threatening passing options mid way inside the opposition half and just leftfield. THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: HOW TO FRAME AN INDIVIDUAL MATCH OUTCOME. Here's some representative figures. Home teams are scoring 0.25 goals per game more than visitors, 1.49 compared to 1.24. The average game has 1.37 expected goals per team. THE POWER OF GOALS.: LIVERPOOL BY ONE. Liverpool won 10 games by a single goal margin last season. That’s a lot, but well below the single season record held by Manchester United of 16 in 2012/13 and 2008/09. THE POWER OF GOALS.: THE CASE FOR CROSSES. The recently departed Euro Finals provided a paradox for advocates of different styles of play. Spain largely did away with the conventional centre forward, choosing instead to play intricate, short passes in the final third while patiently waiting for an opening to appear. THE POWER OF GOALS.: SCORING EFFICIENCY AND CURRENT SCORE. The tie appeared remarkable for many reasons.Not only had Chelsea played over half the second leg with just ten men and Messi had missed from the spot,but they had also enjoyed less than twenty percent of the possession and had been out shot by a ratio of 3:1over both legs.They had managed just 3 shots compared to Barca's 14 in their 1-0 win at Stamford Bridge and had fared only THE POWER OF GOALS.: OCTOBER 2016 At the dawn of footballing time, managers were lasting on average for around 150 matches, now it's down to about 50. Success rate obviously plays a part in perceived managerial talent and Zenga's so so 47% success rate would typically entitle him to at least a season of honest toil, rather than the 17 matches he was actually granted. THE POWER OF GOALS.: 2011 The first thing to notice is that there is a large variation around the average value of 0.42 goals for the league,Manchester United appear to have a massive preference playing at home compared to on the road,while near neighbours Wigan actually performed better away from their home turf.This quite naturally can lead to the impression that better teams manage to muster larger than average### THE POWER OF GOALS.

The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: STATE OF PLAY 2020 Ian’s recent appearances in both the spoken and written media has not only highlighted the importance of an integrated approach to squad building that utilizes a data driven approach, alongside more traditional methods, it has also given a small glimpse into the analytical methods employed. THE POWER OF GOALS.: PREDICTING AND EXPLAINING.HOW TO USE The first choice to make is to decide how to measure success in soccer.Wins are the obvious and overwhelmingly popular selection,but this way fails to account for around 28% of the games that result in a draw.Therefore I have used THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Based on the data and the individual pass expectations, Bolton had about a 1% chance of completing such a move and the last four passes were among the sequence's most difficult attempts. Eagles had around a 10% chance of scoring with his effort. Once we put all these numbers together it quickly becomes apparent why football is a low scoring### sport!

THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 to### H1001.

THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is a### handy

THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or her### actual output.

THE POWER OF GOALS.: HOW RED CARDS AFFECT A SOCCER MATCH. One of the most effective ways to model the expected progress of a soccer match is to calculate the average number of goals each team can expect to score at today's venue and against today's opponents.We can then use the Poisson distribution (with few tweaks) to calculate a multitude of predictions about the likely route the game will take.This is the basis for the game graphs on this site### THE POWER OF GOALS.

The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: STATE OF PLAY 2020 Ian’s recent appearances in both the spoken and written media has not only highlighted the importance of an integrated approach to squad building that utilizes a data driven approach, alongside more traditional methods, it has also given a small glimpse into the analytical methods employed. THE POWER OF GOALS.: PREDICTING AND EXPLAINING.HOW TO USE The first choice to make is to decide how to measure success in soccer.Wins are the obvious and overwhelmingly popular selection,but this way fails to account for around 28% of the games that result in a draw.Therefore I have used THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Based on the data and the individual pass expectations, Bolton had about a 1% chance of completing such a move and the last four passes were among the sequence's most difficult attempts. Eagles had around a 10% chance of scoring with his effort. Once we put all these numbers together it quickly becomes apparent why football is a low scoring### sport!

THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 to### H1001.

THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is a### handy

THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or her### actual output.

THE POWER OF GOALS.: HOW RED CARDS AFFECT A SOCCER MATCH. One of the most effective ways to model the expected progress of a soccer match is to calculate the average number of goals each team can expect to score at today's venue and against today's opponents.We can then use the Poisson distribution (with few tweaks) to calculate a multitude of predictions about the likely route the game will take.This is the basis for the game graphs on this site THE POWER OF GOALS.: 2020 Stoke Highlight the Art of Crossing. Two Stoke City games, two headers, two goals and a duo of 1-0 wins not only demonstrates the fine lines that can separate six points from two in a low scoring sport, such as football, but also the important role still played by crosses in the modern game. THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. To maintain a goal expectation of 1.2 goals for each side, I gave each shot a 10% likelihood of success. So it is an artificial situation, but hopefully a test of the effect of goal expectation being unevenly spread among varying shot quantity. Potentially, team A, based on just two goal attempts can only score a maximum of two goals in a shot THE POWER OF GOALS.: QUANTIFYING THE VALUE OF EVERY PASS This represents the starting point of every successful pass. The plot is best used in conjunction with video analysis, but you can quickly see that Rice's sphere of influence is concentrated broadly in front of the back four and across the line, but he also delivers an impressive range of threatening passing options mid way inside the opposition half and just leftfield. THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: HOW TO FRAME AN INDIVIDUAL MATCH OUTCOME. Here's some representative figures. Home teams are scoring 0.25 goals per game more than visitors, 1.49 compared to 1.24. The average game has 1.37 expected goals per team. THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: LLORIS, THE BEST WITH ROOM TO IMPROVE? An average keeper gets more than a look in in this subset and the average model equals or beats Lloris' far post, on target actual outcome around 22% of the time. That's still ok, but perhaps suggests that even the very best have room to improve. Below I've stitched together a handful of Lloris' attempts to keep out far post, cross### shots to

THE POWER OF GOALS.: PREDICTING THE RARE AND SIGNIFICANT Predicting the Rare and Significant from the Incidental and Commonplace. The transfer window remains open and the net is awash with statistical comparisons between present incumbents and perceived replacements, but with a few notable exceptions, much of this fevered speculation fails to address the problems of sample size in the data### used.

THE POWER OF GOALS.: "IT'S ALL ABOUT THE DISTRIBUTION PART 2" First the disclaimer, this isn't a "smart after the event" explanation for Leicester's title season. It is a list of the occasional, nasty or pleasant surprises that can occur and the limitations of trying to second guess these when using a linear, ratings based model. THE POWER OF GOALS.: SCORING EFFICIENCY AND CURRENT SCORE. The tie appeared remarkable for many reasons.Not only had Chelsea played over half the second leg with just ten men and Messi had missed from the spot,but they had also enjoyed less than twenty percent of the possession and had been out shot by a ratio of 3:1over both legs.They had managed just 3 shots compared to Barca's 14 in their 1-0 win at Stamford Bridge and had fared only### THE POWER OF GOALS.

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THURSDAY, 26 DECEMBER 2019### STATE OF PLAY 2020

Liverpool’s bilingual mastermind behind the team’s meteoric rise to dominate club, domestic, European and now world football is gradually gaining a higher media profile. Not Jurgen Klopp, although he has played a part in the Red’s success, but Dr Ian Graham, their current director of research. Ian’s recent appearances in both the spoken and written media has not only highlighted the importance of an integrated approach to squad building that utilizes a data driven approach, alongside more traditional methods, it has also given a small glimpse into the analytical methods employed. The latest profile landed courtesy of Liverpool.com and described some fundamentals of Liverpool’s analytical### philosophy.

One particularly resonated with Infogol’s approach of quantifying every footballing action in the same currency of goals or more specifically x goals. The idea that every action, be it a pass, tackle or long throw changes the likelihood that a side will ultimately score isn’t a new### concept.

It was probably first introduced into the public analytical domain by Dan Altman in his whistle stop OptaPro presentation in 2015 and hints of such models have been recently emerging from Opta itself and Twelve football . Such a non-shot xG model also powers Infogol’s “Team of the### Week”.

The gradual migration, at least inside the industry, from a purely chance based evaluation to a more holistic one somewhat mirrors the earlier transition from merely counting shots, as exemplified by total shot ratios from 2008 to a more informative, location based xG model,### subsequently.

However, creating such non-shot models that quantify every on-field action is not a simple task. The granular data required to build non-shot models dwarfs that that was needed to create TSR, which itself was rudimentary and basic compared to that required to create a proficient xG model. These leaps in data driven evaluation presents a dilemma for the aspirations of public and hobbyist analysts, an area that provided much of the driving force behind the early explosion in football### analytics.

Latterly, monetization of ideas and a larger appetite for quantitative metrics to supplement opinion driven insight in the media and clubs, has swept many of those same hobbyists behind a non-disclosure paywall. Less co-operation, dwindling numbers, availability of adequate data and the need for diverse technical skills to process that raw data, appears to have stifled the growth of football metrics in the purely### public arena.

At the risk of falling victim to one of Twitter’s sloganized insults, “back in the day, metrics didn’t last long before they were improved upon or supplanted altogether”. Liverpool.com suggested that Ian’s weapons grade model might be broadly replicated by current, readily available and much quoted metrics, such as xG Chain (I’ll let you google the definition). Succinctly, the metric rewards every participant in a move that ends in a goal attempt with that chance’s entire xG. The distribution of goodies can seem churlish, for example, by giving far less individual credit to the three Middlesbrough players who swept nearly the length of Stoke’s defensive transition to score a low probability winner on Friday night, as it would a marginally involved square ball on route to a multiple passing move that ends with a tap in from six yards. More crucially it completely omits actions that aren’t concluded by### a created chance.

To test Liverpool.com’s optimism, I compared Infogol’s non-shot ball progression via passes and carries to the much-touted gold standard of xG Chain. To avoid confusion over units, I’ve simply ranked the xG Chain and the non-shot ball progression for each player in the recent Merseyside derby and then compared a player’s rank in one metric with his rank### in the other.

It starts off quite well. Sadio Mane ranks top in both, he was outstanding on the night. But then, much like Stoke’s trip to Middlesborough, things take a turn for the worse. Shaqiri ranked an impressive 2nd overall in ball progression, but a lowly 16th in xG Chain, whereas Origi rates highly by the latter, but much less so in the former. Overall, a third of the players have double digit ranking differences between their pecking order in both metrics. There are some agreements, but the relationship between the two metrics is generally### weak.

Extend the study to every game played last season and this tenuous correlation between the two metrics remains. One of the strengths of the early analytics movement was the ability to sift mere statistical trivia (team Y has recorded X when player Z plays, immediately springs to mind) from useful, if imperfect evaluations that convey insight and can be used to both evaluate and project future performance. A great example of the latter is Dan Kennett’s recent Allisson tweet, which used big chances to highlight the keeper’s importance to Liverpool, both in the past and possibly in the future. Save rates when faced with Opta’s Big Chances can be framed to be a very good proxy for a more exhaustive and granular, post shot xG2 modelling of a keepers saves and goals allowed. Dan’s tweet was selective, but also carefully constructed enough to capture the keeper’s core attributes. Current retweets are approaching around 10 billion! That should be the benchmark for widely used metrics and player contribution figures, such as xG Chain fail that test on numerous### counts.

It fails to differentiate individual contribution, omits larger swaths of creditable actions and thus fails to correlate well with more exhaustive modelling of a similar player process. The challenge for the public arena as we enter the roaring 20’s is to come up with constant improvements to substandard and potentially misleading measures….. and be more like Dan. __ Posted by Mark Taylor at 10:54### 0 comments

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TUESDAY, 29 OCTOBER 2019### LIVERPOOL BY ONE.

Old style goals based analysis hardly gets a run out nowadays with everyone arguing xG strawmen. So, let’s go the goals route to see if Liverpool’s record in single goal margin wins is “knowing how to win”, “unsustainable” or “about what you’d expect”. Liverpool won 10 games by a single goal margin last season. That’s a lot, but well below the single season record held by Manchester United of 16 in 2012/13 and 2008/09. United’s number of single goal wins in those subsequent seasons fell to five and eight respectively (although something more impactful may have also occurred in 2013/14). Their points tally fell as well, by 25 points in 2013/14 and by 5 in 2009/10. To dilute the Fergie/Moyes effect, let’s look at the average record in the next season of teams who won 10 or more games by a single### margin.

There’s over 90 of them during the 20 team history of the Premier League and 80% of those had fewer wins by the narrowest possible of margins during their next Premier League season, 74% also saw their### points total fall.

These teams who edged lots of close matches one season shed around 10% of their points in the next season. Initially, it’s not looking too rosy for Liverpool’s ability to sustain these narrow wins. However, there’s another factor to consider. Single goal wins, on average account for 41% of a side’s Premier League points total, but in our sample of 90+ teams who won 10 or more, 80% of them accrued more than 41% of their points from such### victories.

Everton won 76% of their 59 points in 2002/03 from single goal wins and then tried their very best to get relegated in 2003/04 as their “luck” in narrow games returned to earth and they won just 39### points.

In Liverpool’s case in 2018/19, one goal margin wins only accounted for 31% of their 97 points. Therefore, their ten such wins places them in a group of sides who typically regress, but the percentage of total points they win in this manner is entirely atypical of that group. To see where Liverpool stand as being adept at winning single goal margin games, we need to look at their underlying goals record. In 2018/19 they scored 89 and conceded 22, taking the Poisson route, that’s consistent with winning nine games by a single goal over 38 games. They won, as we’ve seen ten, hardly a worryingly large### over-performance.

You can lump Liverpool in with a group of teams who have achieved good things, partly as a result of “knowing how to win” (Leicester 2015/16 spring to mind, 14 single goal wins where nine would have been a more equitable return), but unlike most of these sides, the Reds have the underlying numbers to deserve their record. Expect a few more 2-1’s between now and May. __ Posted by Mark Taylor at 09:29### 0

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MONDAY, 21 OCTOBER 2019### CLOSING THE DOOR.

One of the most fun aspects of football data analysis is when the team you're part of derives some exciting newly derived metrics from the raw data that allows you to look at old problems with a new light. Some real heavy data lifting has been put into deriving our Non Shot expected goals model. So first a quick recap on what it does. Whenever the ball is moved around the pitch there is a likelihood of scoring from each location it finds itself in. We express this value as non shot xG and the difference between these values when an action is completed is the change in NSxG via that action. There's also a "risk/reward" aspect for when you concede possession. Finally, each team has (nearly always) a different NSxG for the same pitch location, because one major input is the distance to your### opponents goal.

We've mainly looked at passing and ball carrying, so far, quantifying the differing importance to your side of moving the ball five yards out of your own penalty area or five yards into your opponents. But there's an obvious extension of this that flips the focus and examines how well a team prevents an opponent progression the ball. This isn't just by making passing difficult, it's also by making it harder or easier for opponents to carry the ball forward as well. It used to be call closing a player down, it's called any manner of### terms nowadays.

Here's how sides are fairing in preventing ball progression in### 2019/20.

The first thing you need is a benchmark figure to measure how well a side is closing down the opposition. There's only been nine matches played by each Premier League team to date and they may have played a bunch of sides who aren't that good or willing to play out from the back, so we need to find a set of figures that reflect this possible imbalance of intent and talent. Let's take Manchester United. They've played nine teams, Chelsea, CP, Leicester, Newcastle, Southampton, WHU, Arsenal, Wolves & Liverpool. Those teams, in turn have also played nine teams (except Arsenal, who play tonight), that's 80 teams of which nine are Manchester United. That's almost guaranteed to include every Premier League team at least once and makes up a decent sample of around 70-80 games depending upon### how you slice it.

We therefore, we took those 71 non Manchester United matches played by Manchester United's opponents and looked at the "risk/reward" ball progression via _both_ passes and ball carries for 100 pitch segments. For each segment we calculated the average NS xG gained (or lost) per 100 pass & carry attempts. That was our baseline for United's opponents progression against a broad selection of opponents this### season.

Then we repeated the exercise, but for these sides in their matches against Manchester United and ran a heat map to see where on the field these teams were finding it difficult to progress the ball against United and where they were having a easier time compared to their benchmark numbers against the rest of their opponents. This is what it looks like ( ignore the numbers for now). The red areas are where United's opponents are progressing the ball at lower levels against United than they've managed as a group against a basket of 71 other Premier League sides. Blue, they're doing better. It's a pretty stark and clear picture of where on the field United have been making it difficult for their opponents to get the ball into more dangerous areas. Firstly, beginning in front of their opponent's own box and then aggressively in front of United's own. They aren't too fussed about targeting wide positions on halfway and not too good(?) at stopping runs or passes from the bye-line & in the box. Here's Everton and they do harry the opposition, but it's a much more chaotic process, with very little structure, especially compared to United's disciplined approach. And finally, here's Aston Villa. There's no overt closing down of the opposition until they reach the box, at which point it seems to become all hands to the pump. Posted by Mark Taylor at 10:21### 0

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